Evaluation

MBOin2030

library(here)

here() starts at /Users/maarten/Documents/projects/mbo-rekenen

library(data.table)

Warning: package 'data.table' was built under R version 4.3.3

library(ggplot2)
library(jsonlite)
library(purrr)

Attaching package: 'purrr'

The following object is masked from 'package:jsonlite':

    flatten

The following object is masked from 'package:data.table':

    transpose

library(tidytext)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:data.table':

    between, first, last

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(wordcloud2)

theme_memorylab_url <- "https://raw.githubusercontent.com/SlimStampen/theme_memorylab/master/theme_memorylab.R"
source(theme_memorylab_url)

Evaluation questions

Students answered a number of evaluation questions after the posttest.

Hoe koos je welke les je ging doen?

q1 <- fread(here("data", "feedback", "q1_choice.csv"))
q1[, freq := Totaal / sum(Totaal)]

q1

ggplot(q1, aes(x = reorder(Antwoord, freq), y = freq)) +
  geom_col(fill = colours_memorylab[1]) +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(title = "Hoe koos je welke les je ging doen?",
       x = NULL,
       y = NULL) +
  coord_flip() +
  theme_ml() +
  theme(panel.grid.major.x = element_line(colour = "grey90"))

Loading required package: showtext

Loading required package: sysfonts

Loading required package: showtextdb

Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

ggsave(here("output", "evaluatie_leskeuze.png"), width = 10, height = 4)

What proportion of students made a choice based on difficulty?

q1[1:3, sum(freq)]

[1] 0.606383

Bij welk onderwerp vond je MemoryLab het meest toevoegen?

q2 <- fread(here("data", "feedback", "q2_topic.csv"), header = TRUE)
q2_long <- melt(q2, measure.vars = 2:12, variable.name = "positie", value.name = "N")
q2_long[, positie := as.numeric(positie)]

# Calculate the average position by topic
q2_rank <- q2_long[, .(mean_rank = weighted.mean(positie, N)), by = "topic"]

ggplot(q2_rank, aes(x = reorder(topic, -mean_rank), y = mean_rank)) +
  geom_col(fill = colours_memorylab[1]) +
  labs(title = "Bij welk onderwerp vond je MemoryLab het meest toevoegen?",
       x = NULL,
       y = "Gemiddelde rang (lager is beter)") +
  coord_flip() +
  theme_ml() +
  theme(panel.grid.major.x = element_line(colour = "grey90"))

ggsave(here("output", "evaluatie_onderwerp.png"), width = 10, height = 4)

Geef MemoryLab een cijfer

q3 <- fread(here("data", "feedback", "q3_grade.csv"))
q3[, freq := Totaal / sum(Totaal)]

# Mean grade
q3[, weighted.mean(grade, Totaal)]

[1] 6.561224

# What proportion of students gave a passing grade?
q3[grade >= 6, sum(freq)]

[1] 0.8265306

ggplot(q3, aes(x = grade, y = freq)) +
  geom_col(fill = colours_memorylab[1]) +
  scale_x_continuous(breaks = 1:10) +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(title = "Geef MemoryLab een cijfer",
       x = "Cijfer",
       y = NULL) +
  theme_ml()

ggsave(here("output", "evaluatie_cijfer.png"), width = 10, height = 4)

Zou je deze leermethode ook willen gebruiken voor andere rekenlesstof?

q4  <- fread(here("data", "feedback", "q4_useother.csv"))
q4[, freq := Totaal / sum(Totaal)]

q4

Wat vond je fijn aan het oefenen met MemoryLab?

stop_words <- get_stopwords(language = "nl")

# Read text file 
q1 <- readLines(here("data", "feedback", "Q_watvondjefijn.txt"))

q1_words <- tibble(answer = tolower(q1)) |>
  unnest_tokens(word, answer) |>
  anti_join(stop_words, by = "word") |>
  count(word, sort = TRUE)

# Make wordcloud
wordcloud2(q1_words,
           shuffle = FALSE,
           minSize = 4,
           size = 4,
           rotateRatio = .4
           )

# Take screenshot of viewer to save

Wat vond je niet fijn aan het oefenen met MemoryLab?

q2 <- readLines(here("data", "feedback", "Q_watvondjenietfijn.txt"))

q2_words <- tibble(answer = tolower(q2)) |>
  unnest_tokens(word, answer) |>
  anti_join(stop_words, by = "word") |>
  count(word, sort = TRUE)

# Make wordcloud
wordcloud2(q2_words,
           shuffle = FALSE,
           minSize = 4,
           size = 4,
           rotateRatio = .4
)

# Take screenshot of viewer to save